Generalized saddle-point and non-Hermitian partitioned systems

GPMR

Krylov.gpmr — Function

(x, y, stats) = gpmr(A, B, b::AbstractVector{FC}, c::AbstractVector{FC};
                     memory::Int=20, C=I, D=I, E=I, F=I,
                     ldiv::Bool=false, gsp::Bool=false,
                     λ::FC=one(FC), μ::FC=one(FC),
                     reorthogonalization::Bool=false, atol::T=√eps(T),
                     rtol::T=√eps(T), itmax::Int=0,
                     timemax::Float64=Inf, verbose::Int=0, history::Bool=false,
                     callback=solver->false, iostream::IO=kstdout)

T is an AbstractFloat such as Float32, Float64 or BigFloat. FC is T or Complex{T}.

(x, y, stats) = gpmr(A, B, b, c, x0::AbstractVector, y0::AbstractVector; kwargs...)

GPMR can be warm-started from initial guesses x0 and y0 where kwargs are the same keyword arguments as above.

Given matrices A of dimension m × n and B of dimension n × m, GPMR solves the non-Hermitian partitioned linear system

[ λIₘ   A  ] [ x ] = [ b ]
[  B   μIₙ ] [ y ]   [ c ],

of size (n+m) × (n+m) where λ and μ are real or complex numbers. A can have any shape and B has the shape of Aᴴ. A, B, b and c must be all nonzero.

This implementation allows left and right block diagonal preconditioners

[ C    ] [ λM   A ] [ E    ] [ E⁻¹x ] = [ Cb ]
[    D ] [  B  μN ] [    F ] [ F⁻¹y ]   [ Dc ],

and can solve

[ λM   A ] [ x ] = [ b ]
[  B  μN ] [ y ]   [ c ]

when CE = M⁻¹ and DF = N⁻¹.

By default, GPMR solves unsymmetric linear systems with λ = 1 and μ = 1.

GPMR is based on the orthogonal Hessenberg reduction process and its relations with the block-Arnoldi process. The residual norm ‖rₖ‖ is monotonically decreasing in GPMR.

GPMR stops when itmax iterations are reached or when ‖rₖ‖ ≤ atol + ‖r₀‖ * rtol. atol is an absolute tolerance and rtol is a relative tolerance.

Input arguments

A: a linear operator that models a matrix of dimension m × n;
B: a linear operator that models a matrix of dimension n × m;
b: a vector of length m;
c: a vector of length n.

Optional arguments

x0: a vector of length m that represents an initial guess of the solution x;
y0: a vector of length n that represents an initial guess of the solution y.

Keyword arguments

memory: if restart = true, the restarted version GPMR(k) is used with k = memory. If restart = false, the parameter memory should be used as a hint of the number of iterations to limit dynamic memory allocations. Additional storage will be allocated if the number of iterations exceeds memory;
C: linear operator that models a nonsingular matrix of size m, and represents the first term of the block-diagonal left preconditioner;
D: linear operator that models a nonsingular matrix of size n, and represents the second term of the block-diagonal left preconditioner;
E: linear operator that models a nonsingular matrix of size m, and represents the first term of the block-diagonal right preconditioner;
F: linear operator that models a nonsingular matrix of size n, and represents the second term of the block-diagonal right preconditioner;
ldiv: define whether the preconditioners use ldiv! or mul!;
gsp: if true, set λ = 1 and μ = 0 for generalized saddle-point systems;
λ and μ: diagonal scaling factors of the partitioned linear system;
reorthogonalization: reorthogonalize the new vectors of the Krylov basis against all previous vectors;
atol: absolute stopping tolerance based on the residual norm;
rtol: relative stopping tolerance based on the residual norm;
itmax: the maximum number of iterations. If itmax=0, the default number of iterations is set to m+n;
timemax: the time limit in seconds;
verbose: additional details can be displayed if verbose mode is enabled (verbose > 0). Information will be displayed every verbose iterations;
history: collect additional statistics on the run such as residual norms, or Aᴴ-residual norms;
callback: function or functor called as callback(solver) that returns true if the Krylov method should terminate, and false otherwise;
iostream: stream to which output is logged.

Output arguments

x: a dense vector of length m;
y: a dense vector of length n;
stats: statistics collected on the run in a SimpleStats structure.

Reference

A. Montoison and D. Orban, GPMR: An Iterative Method for Unsymmetric Partitioned Linear Systems, SIAM Journal on Matrix Analysis and Applications, 44(1), pp. 293–311, 2023.

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Krylov.gpmr! — Function

solver = gpmr!(solver::GpmrSolver, A, B, b, c; kwargs...)
solver = gpmr!(solver::GpmrSolver, A, B, b, c, x0, y0; kwargs...)

where kwargs are keyword arguments of gpmr.

Note that the memory keyword argument is the only exception. It's required to create a GpmrSolver and can't be changed later.

See GpmrSolver for more details about the solver.

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